Computer-assisted surgical planning and advanced image-guided technology are increasingly used in neurosurgery, as the availability of accurate anatomical 3-dimensional models improves considerably the spatial information concerning the relationships of important structures. In addition, the importance of computer-aided diagnosis in neuroradiology is growing. At the same time, brain databases are expanding rapidly and the technology to analyse these images efficiently, particularly for large databases, is progressing.
The identification of brain tumours has conventionally been achieved through segmentation using knowledge-based systems or an atlas, as set out in Kaus M R et al “Automated segmentation of MR images of brain tumours” Radiology 2001; 218(2): 586-591; Fletcher-Heath L M et al, “Automatic Segmentation of non-enhancing brain tumors in magnetic resonance image,” Artificial Intelligence in Medicine 2001; 21(1-3): 43-63; and Clark M C et al, “Automatic tumor segmentation using knowledge-based technique” IEEE Transactions on Medical Imaging 1998; 17(2): 187-201.
U.S. Pat. No. 4,856,528 discloses a computer-implemented arrangement for semi-automatically determining the volume of a tumour from CT image data. A histogram indicative of the number of pixels within the organ outline of respective slices is produced. The distinction between the tumour tissue and normal organ tissue is determined enabling determination of the volume of the tumour.
WO 02/43003 discloses a system for analysing a brain image. The brain image is compared with a brain atlas and the image is labelled accordingly, and annotated with regions of interest and/or other structures.
Eur J Radiol 2003 March; 45(3):199-207 entitled “Characterization of normal brain and brain tumor pathology by chisquares parameter maps of diffusion-weighted image data” Maier S E, Mamata H, Mulkern R V, Dept. of Radiology (MRI), Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, 02115 Boston Mass., USA discloses the test results of a characterisation of normal and pathologic brain tissue by quantifying the deviation of a diffusion-related signal from a monoexponential decay when measured over a range of b-factors.
The present invention is directed to identifying pathology in medical images and automated scan interpretation. This is critical when searching large brain databases and the present invention aims to provide a method for fast identification of pathology in images. The method is particularly advantageous in situations where there is a need to identify the presence of pathology before model-based approaches may be applied. The pathology may then be localised and segmented prior to the application of such model-based approaches.